A mobility firm based in San Francisco is pioneering how urban mobility data can shape the future of electric air taxis. The organization, working with mobility analytics company CITYDATA.ai, has developed a framework that uses anonymized GPS movement data to help estimate eVTOL (electric vertical take-off and landing) fleet needs for future urban air mobility (UAM) networks.
The system analyzes terrestrial traffic demand using real-world travel data to suggest optimal locations for vertiports and simulate how many eVTOLs would be needed under different adoption scenarios. The model also allows users to adjust assumptions like trip conversion rates and aircraft types (such as models from Joby, Archer, Volocopter, or Eve) to calculate dynamic fleet requirements.
Experts say this approach could help avoid infrastructure overinvestment and better align public-private efforts in UAM integration. While many cities are exploring how air taxis could relieve congestion, few have grounded their planning in actual urban mobility patterns.
CITYDATA.ai’s pilot program includes tools for scenario testing and visual analytics, and although still in a prototype phase, stakeholders in airport planning and local government have reportedly shown interest.
The effort reflects a growing recognition that data-driven planning is key to realizing the promises of urban air mobility, whether for city-wide service or targeted, high-demand corridors.
For more information, visit CITYDATA.ai